As is known, the fact that no two fingerprints are alike and therefore represent a sort of identity card results in a wide range of applications. For example, in addition to being traditionally used in crime detection, it has also been suggested to use fingerprints as a sort of personal key or signature to improve the safety of credit cards and control access to security areas, computers or banking systems, and also as a substitute for cumbersome house or car keys.
The difficulty in identifying fingerprints lies in defining a score assignable to a pair of fingerprint images and expressing the likelihood that both belong to the same person. Moreover, the two fingerprints for comparison are acquired at different times, one normally forming part of a data bank containing references to known persons, and the other being validated or identified by comparison with the first.
Traditional fingerprint identification methods, which are normally performed manually by experts, comprise numerous classification steps. In recent times, however, automatic methods have been proposed, some of which comprise preclassification steps similar to those of traditional methods, while others dispense entirely with such steps and, commencing directly from the fingerprint image obtained, for example, by means of a scanner or sensor, provide for identification by appropriate processing. The present invention relates to the second type of approach.
A fully automatic identification system is described, for example, in "Automatic Fingerprint Identification" by K. Asai, Y. Kato, Y. Hoshino and K. Kiji, Proceedings of the Society of Photo-Optical Instrumentation Engineers, vol. 182, Imaging Applications for Automated Industrial Inspection and Assembly, p. 49-56, 1979. According to this system, the significant points in the image (epidermal ridge terminations and bifurcations, known as "minutiae") are determined directly from the image in tones of grey, and matching of the prints is determined on the basis of a ridge count and the direction of the minutiae, as in the manual method.
In "Fingerprint Identification using Graph Matching" by D. K. Isenor and S. G. Zaky, Pattern Recognition, vol. 19, no. 2, p. 113-122, 1986, a graph matching method is presented whereby the epidermal ridges and grooves are located in the print image, the ridges being numbered and oriented, and the grooves forming the background; a degree of adjacency is defined indicating which ridge is adjacent to which other; any irregularities due to soiling or interrupted ridges due to the acquisition system are identified and corrected on the graph to obtain a final graph representing the print in coded form; and the similarity of the prints is determined by comparing the graphs.
In "Automated Fingerprint Identification by Minutia-network Feature-Matching Processes" by K. Asai, Y. Hoshino and K. Kiji, Transaction of the Institute of Electronics, Information and Communication Engineers D-II, vol. J72D-II, no. 5, p. 733-740, May 1989 (in Japanese), use is made of a minutia-network containing termination and bifurcation points, and the epidermal ridges are counted to relate the significant adjacent points. The local direction of the epidermal ridges is also taken into consideration, and the pairs of matching points are obtained by coordinate transformation and similarity computation.
Assessed using a few hundred known prints, the above systems do not yet provide for the degree of accuracy required for police work involving the comparison of millions of images.